{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction to QRenderers\n",
    "\n",
    "For convenience, let's begin by enabling [automatic reloading of modules](https://ipython.readthedocs.io/en/stable/config/extensions/autoreload.html?highlight=autoreload) when they change."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Qiskit Metal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import qiskit_metal as metal\n",
    "from qiskit_metal import designs, draw\n",
    "from qiskit_metal import MetalGUI, Dict, Headings\n",
    "\n",
    "from qiskit_metal.qlibrary.qubits.transmon_pocket import TransmonPocket\n",
    "from qiskit_metal.qlibrary.qubits.transmon_cross import TransmonCross\n",
    "\n",
    "from qiskit_metal.renderers.renderer_gds.gds_renderer import QGDSRenderer "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "     <h1 style=\"         background-color: #d4418e;         background-image: linear-gradient(315deg, #d4418e 0%, #0652c5 74%);         margin-top: 50px;         border-style: outset;         padding-top:100px;         padding-bottom:50px;         padding-left:25px;         color: white;     \"> The default_options in a QComponent are different than the default_options in QRenderers. <h1>     "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Headings.h1('The default_options in a QComponent are different than the default_options in QRenderers.')\n"
   ]
  },
  {
   "attachments": {
    "a3b155b8-23ee-493a-b9d2-40e2d51aef9b.jpg": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![QDesign Data Flow_accurate.jpg](attachment:a3b155b8-23ee-493a-b9d2-40e2d51aef9b.jpg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'chip': 'main',\n",
       " 'pos_x': '0um',\n",
       " 'pos_y': '0um',\n",
       " 'pad_gap': '30um',\n",
       " 'inductor_width': '20um',\n",
       " 'pad_width': '455um',\n",
       " 'pad_height': '90um',\n",
       " 'pocket_width': '650um',\n",
       " 'pocket_height': '650um',\n",
       " 'orientation': '0',\n",
       " '_default_connection_pads': {'pad_gap': '15um',\n",
       "  'pad_width': '125um',\n",
       "  'pad_height': '30um',\n",
       "  'pad_cpw_shift': '5um',\n",
       "  'pad_cpw_extent': '25um',\n",
       "  'cpw_width': 'cpw_width',\n",
       "  'cpw_gap': 'cpw_gap',\n",
       "  'cpw_extend': '100um',\n",
       "  'pocket_extent': '5um',\n",
       "  'pocket_rise': '65um',\n",
       "  'loc_W': '+1',\n",
       "  'loc_H': '+1'}}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TransmonPocket.default_options\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'short_segments_to_not_fillet': 'True',\n",
       " 'check_short_segments_by_scaling_fillet': '2.0',\n",
       " 'gds_unit': '1',\n",
       " 'ground_plane': 'True',\n",
       " 'negative_mask': {'main': []},\n",
       " 'corners': 'circular bend',\n",
       " 'tolerance': '0.00001',\n",
       " 'precision': '0.000000001',\n",
       " 'width_LineString': '10um',\n",
       " 'path_filename': '../resources/Fake_Junctions.GDS',\n",
       " 'junction_pad_overlap': '5um',\n",
       " 'max_points': '199',\n",
       " 'cheese': {'datatype': '100',\n",
       "  'shape': '0',\n",
       "  'cheese_0_x': '25um',\n",
       "  'cheese_0_y': '25um',\n",
       "  'cheese_1_radius': '100um',\n",
       "  'view_in_file': {'main': {1: True}},\n",
       "  'delta_x': '100um',\n",
       "  'delta_y': '100um',\n",
       "  'edge_nocheese': '200um'},\n",
       " 'no_cheese': {'datatype': '99',\n",
       "  'buffer': '25um',\n",
       "  'cap_style': '2',\n",
       "  'join_style': '2',\n",
       "  'view_in_file': {'main': {1: True}}},\n",
       " 'bounding_box_scale_x': '1.2',\n",
       " 'bounding_box_scale_y': '1.2'}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "QGDSRenderer.default_options"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A renderer needs to inherent from QRenderer\n",
    "For Example, QGDSRender inherents from QRenderer.\n",
    "\n",
    "When any QRenderer is registered within QDesign, the QRenderer instance has options, which hold the latest set of values for default_options.  The GUI can also update these options. \n",
    "\n",
    "An example of updating options is further below in this notebook.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A user can customize things two ways\n",
    "\n",
    "1.  Directly update the options that originated from default_options, for either QComponent or QRenderer.\n",
    "\n",
    "2.  Pass options to a QComponent which will be placed in a QGeometry table, then used by QRenderer."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How to get options from QRenderer to be placed within the QGeometry table?\n",
    "We set this up so that older QComponents can be agnostic of newer QRenderers. \n",
    "\n",
    "The \"rate limiting factor\" is to have QComponent denote in it's metadata, which QGeometry tables it will write to.  For this example, we will discuss the \"junction\" table.  More details will be in the notebook at \"tutorials/4 Plugin Developer\".  \n",
    "If the QComponent identifies the table which it is aware of, and if QGDSRenderer wants to add a column to the table with a default value, then QComponent will pass the option from QGDSRenderer to QGeometry table without doing anything with it.\n",
    "\n",
    "An example of this below is `gds_cell_name='FakeJunction_01'`.  This is passed through to QGeometry, when a QComponent is instantiated.  The QGDSRenderer has a default, which is not editable during run-time, but can be customized when a QComponent is instantiated.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "     <h1 style=\"         background-color: #d4418e;         background-image: linear-gradient(315deg, #d4418e 0%, #0652c5 74%);         margin-top: 50px;         border-style: outset;         padding-top:100px;         padding-bottom:50px;         padding-left:25px;         color: white;     \"> How does a QRenderer get registered within QDesign? <h1>     "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Headings.h1('How does a QRenderer get registered within QDesign?')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### By default, QRenderers are registered within QDesign during init QDesign\n",
    "The list of QRenderers which will be registered are in qiskit_metal.config.py;   \n",
    "the dictionary `renderers_to_load` has the name of the QRenderer (key), class name (value), and path (value).\n",
    "\n",
    "Presently, GDS and Ansys QRenderers are registered during init.  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "design = designs.DesignPlanar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use GDS QRenderer for remaining examples.  Can do similar things with Ansys QRenderer.\n",
    "\n",
    "#an_ansys = design._renderers['ansys']\n",
    "#an_ansys = design._renderers.ansys\n",
    "\n",
    "#a_gds = design._renderers['gds']\n",
    "a_gds = design._renderers.gds\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "gui = MetalGUI(design)\n",
    "design.overwrite_enabled = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "     <h1 style=\"         background-color: #d4418e;         background-image: linear-gradient(315deg, #d4418e 0%, #0652c5 74%);         margin-top: 50px;         border-style: outset;         padding-top:100px;         padding-bottom:50px;         padding-left:25px;         color: white;     \"> Populate QDesign to demonstrate exporting to GDS format. <h1>     "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Headings.h1('Populate QDesign to demonstrate exporting to GDS format.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit_metal.qlibrary.qubits.transmon_pocket import TransmonPocket\n",
    "\n",
    "# Allow running the same cell here multiple times to overwrite changes\n",
    "design.overwrite_enabled = True\n",
    "\n",
    "## Custom options for all the transmons\n",
    "options = dict(\n",
    "    # Some options we want to modify from the deafults\n",
    "    # (see below for defaults)\n",
    "    pad_width = '425 um',\n",
    "    pad_gap = '80 um',\n",
    "    pocket_height = '650um',\n",
    "    # Adding 4 connectors (see below for defaults)\n",
    "    connection_pads=dict( \n",
    "        a = dict(loc_W=+1,loc_H=+1), \n",
    "        b = dict(loc_W=-1,loc_H=+1, pad_height='30um'),\n",
    "        c = dict(loc_W=+1,loc_H=-1, pad_width='200um'),\n",
    "        d = dict(loc_W=-1,loc_H=-1, pad_height='50um')\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Note:  \n",
    "The cell name denoted by, \"gds_cell_name\" will be the selected cell   \n",
    "from design.renderers.gds.options['path_filename']   \n",
    "when design.renderers.gds.export_to_gds() is executed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Create 4 TransmonPockets\n",
    "\n",
    "q1 = TransmonPocket(design, 'Q1', options = dict(\n",
    "    pos_x='+2.55mm', pos_y='+0.0mm', gds_cell_name='FakeJunction_02', **options))\n",
    "q2 = TransmonPocket(design, 'Q2', options = dict(\n",
    "    pos_x='+0.0mm', pos_y='-0.9mm', orientation = '90', gds_cell_name='FakeJunction_02', **options))\n",
    "q3 = TransmonPocket(design, 'Q3', options = dict(\n",
    "    pos_x='-2.55mm', pos_y='+0.0mm', gds_cell_name='FakeJunction_01',**options))\n",
    "q4 = TransmonPocket(design, 'Q4', options = dict(\n",
    "    pos_x='+0.0mm', pos_y='+0.9mm', orientation = '90', gds_cell_name='my_other_junction', **options))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "## Rebuild the design\n",
    "gui.rebuild()\n",
    "gui.autoscale()\n",
    "\n",
    "#Connect using techniques explained earlier notebooks.\n",
    "\n",
    "from qiskit_metal.qlibrary.tlines.meandered import RouteMeander\n",
    "RouteMeander.get_template_options(design)\n",
    "\n",
    "options = Dict(\n",
    "    meander=Dict(\n",
    "        lead_start='0.1mm',\n",
    "        lead_end='0.1mm',\n",
    "        asymmetry='0 um')\n",
    ")\n",
    "\n",
    "\n",
    "def connect(component_name: str, component1: str, pin1: str, component2: str, pin2: str,\n",
    "            length: str, asymmetry='0 um', flip=False, fillet='50um'):\n",
    "    \"\"\"Connect two pins with a CPW.\"\"\"\n",
    "    myoptions = Dict(\n",
    "        fillet=fillet,\n",
    "        pin_inputs=Dict(\n",
    "            start_pin=Dict(\n",
    "                component=component1,\n",
    "                pin=pin1),\n",
    "            end_pin=Dict(\n",
    "                component=component2,\n",
    "                pin=pin2)),\n",
    "        lead=Dict(\n",
    "            start_straight='0.13mm',\n",
    "            end_straight='0.13mm'\n",
    "        ),\n",
    "        total_length=length)\n",
    "    myoptions.update(options)\n",
    "    myoptions.meander.asymmetry = asymmetry\n",
    "    myoptions.meander.lead_direction_inverted = 'true' if flip else 'false'\n",
    "    return RouteMeander(design, component_name, myoptions)\n",
    "\n",
    "\n",
    "asym = 90\n",
    "cpw1 = connect('cpw1', 'Q1', 'd', 'Q2', 'c', '5.7 mm', f'+{asym}um', fillet='25um')\n",
    "cpw2 = connect('cpw2', 'Q3', 'c', 'Q2', 'a', '5.4 mm', f'-{asym}um', flip=True, fillet='100um')\n",
    "cpw3 = connect('cpw3', 'Q3', 'a', 'Q4', 'b', '5.3 mm', f'+{asym}um', fillet='75um')\n",
    "cpw4 = connect('cpw4', 'Q1', 'b', 'Q4', 'd', '5.5 mm', f'-{asym}um', flip=True)\n",
    "\n",
    "gui.rebuild()\n",
    "gui.autoscale()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "tags": [
     "nbsphinx-thumbnail"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "image/png": {
       "width": 500
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gui.screenshot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "     <h1 style=\"         background-color: #d4418e;         background-image: linear-gradient(315deg, #d4418e 0%, #0652c5 74%);         margin-top: 50px;         border-style: outset;         padding-top:100px;         padding-bottom:50px;         padding-left:25px;         color: white;     \"> Exporting a GDS file. <h1>     "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Headings.h1('Exporting a GDS file.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'short_segments_to_not_fillet': 'True',\n",
       " 'check_short_segments_by_scaling_fillet': '2.0',\n",
       " 'gds_unit': 0.001,\n",
       " 'ground_plane': 'True',\n",
       " 'negative_mask': {'main': []},\n",
       " 'corners': 'circular bend',\n",
       " 'tolerance': '0.00001',\n",
       " 'precision': '0.000000001',\n",
       " 'width_LineString': '10um',\n",
       " 'path_filename': '../resources/Fake_Junctions.GDS',\n",
       " 'junction_pad_overlap': '5um',\n",
       " 'max_points': '199',\n",
       " 'cheese': {'datatype': '100',\n",
       "  'shape': '0',\n",
       "  'cheese_0_x': '25um',\n",
       "  'cheese_0_y': '25um',\n",
       "  'cheese_1_radius': '100um',\n",
       "  'view_in_file': {'main': {1: True}},\n",
       "  'delta_x': '100um',\n",
       "  'delta_y': '100um',\n",
       "  'edge_nocheese': '200um'},\n",
       " 'no_cheese': {'datatype': '99',\n",
       "  'buffer': '25um',\n",
       "  'cap_style': '2',\n",
       "  'join_style': '2',\n",
       "  'view_in_file': {'main': {1: True}}},\n",
       " 'bounding_box_scale_x': '1.2',\n",
       " 'bounding_box_scale_y': '1.2'}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#QDesign enables GDS renderer during init.\n",
    "a_gds = design.renderers.gds\n",
    "# An alternate way to envoke the gds commands without using a_gds:\n",
    "# design.renderers.gds.export_to_gds()\n",
    "\n",
    "#Show the options for GDS\n",
    "a_gds.options"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  To make the junction table work correctly, GDS Renderer needs the correct path to the gds file which has cells\n",
    "Each cell is a junction to be placed in a Transmon.  A sample gds file is provided in directory `qiskit_metal/tutorials/resources`.\n",
    "There are three cells with names \"Fake_Junction_01\", \"Fake_Junction_01\", and \"my_other_junction\".\n",
    "The default name used by GDS Render is \"my_other_junction\".  If you want to customize and select a junction, through the options, \n",
    "you can pass it when a qcomponent is being added to QDesign. \n",
    "\n",
    "This allows for an already prepared e-beam pattern for a given junction structure to be automatically imported and placed at the correct\n",
    "location."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "a_gds.options['path_filename'] = '../resources/Fake_Junctions.GDS'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Do you want GDS Renderer to fix any short-segments in your QDesign when using fillet?'\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#If you have a fillet_value and there are LineSegments that are shorter than 2*fillet_value, \n",
    "#When true, the short segments will not be fillet'd. \n",
    "a_gds.options['short_segments_to_not_fillet'] = 'True'\n",
    "scale_fillet = 2.0\n",
    "a_gds.options['check_short_segments_by_scaling_fillet'] = scale_fillet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Export GDS file for all components in design.\n",
    "#def export_to_gds(self, file_name: str, highlight_qcomponents: list = []) -> int:\n",
    "\n",
    "\n",
    "# Please change the path where you want to write a GDS file.\n",
    "#Examples below.  \n",
    "#a_gds.export_to_gds(\"../../../gds-files/GDS QRenderer Notebook.gds\")\n",
    "\n",
    "a_gds.export_to_gds('GDS QRenderer Notebook.gds')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Export a GDS file which contains only few components.\n",
    "\n",
    "# You will probably want to put the exported file in a specific directory.  \n",
    "# Please give the full path for output. \n",
    "a_gds.export_to_gds(\"four_qcomponents.gds\",\n",
    "                           highlight_qcomponents=['cpw1', 'cpw4', 'Q1', 'Q3'])\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How to \"execute\" exporting an QRenderer from GUI vs notebook?\n",
    "Within the GUI, there are icons: GDS, HFSS and Q3D.\n",
    "\n",
    "Example for GDS:\n",
    "Select the components that you want to export from QGeometry Tables. Select the path/file_name and the same thing should happen as the cells above.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "     <h1 style=\"         background-color: #d4418e;         background-image: linear-gradient(315deg, #d4418e 0%, #0652c5 74%);         margin-top: 50px;         border-style: outset;         padding-top:100px;         padding-bottom:50px;         padding-left:25px;         color: white;     \"> QUESTION:  Where is the geometry of a QComponent placed? <h1>     "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Headings.h1('QUESTION:  Where is the geometry of a QComponent placed?')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Answer:  QGeometry tables!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What is QGeometry? \n",
    "\n",
    "###  All QRenderers use the QGeometry tables to export from QDesign.  Each table is a Pandas DataFrame.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can get all the QGeometry of a QComponent. There are several kinds, such as `path`, `poly` and, `junction`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Many ways to view the QGeometry tables.  \n",
    "#If you want to view, uncomment below lines and and run it.\n",
    "\n",
    "#design.qgeometry.tables\n",
    "#design.qgeometry.tables['path']\n",
    "#design.qgeometry.tables['poly']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>component</th>\n",
       "      <th>name</th>\n",
       "      <th>geometry</th>\n",
       "      <th>layer</th>\n",
       "      <th>subtract</th>\n",
       "      <th>helper</th>\n",
       "      <th>chip</th>\n",
       "      <th>width</th>\n",
       "      <th>hfss_inductance</th>\n",
       "      <th>hfss_capacitance</th>\n",
       "      <th>hfss_resistance</th>\n",
       "      <th>hfss_mesh_kw_jj</th>\n",
       "      <th>q3d_inductance</th>\n",
       "      <th>q3d_capacitance</th>\n",
       "      <th>q3d_resistance</th>\n",
       "      <th>q3d_mesh_kw_jj</th>\n",
       "      <th>gds_cell_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>rect_jj</td>\n",
       "      <td>LINESTRING (2.55000 -0.04000, 2.55000 0.04000)</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>main</td>\n",
       "      <td>0.02</td>\n",
       "      <td>10nH</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>10nH</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>FakeJunction_02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>rect_jj</td>\n",
       "      <td>LINESTRING (0.04000 -0.90000, -0.04000 -0.90000)</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>main</td>\n",
       "      <td>0.02</td>\n",
       "      <td>10nH</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>10nH</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>FakeJunction_02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>rect_jj</td>\n",
       "      <td>LINESTRING (-2.55000 -0.04000, -2.55000 0.04000)</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>main</td>\n",
       "      <td>0.02</td>\n",
       "      <td>10nH</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>10nH</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>FakeJunction_01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>rect_jj</td>\n",
       "      <td>LINESTRING (0.04000 0.90000, -0.04000 0.90000)</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>main</td>\n",
       "      <td>0.02</td>\n",
       "      <td>10nH</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>10nH</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>my_other_junction</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  component     name                                          geometry  layer  \\\n",
       "0         1  rect_jj    LINESTRING (2.55000 -0.04000, 2.55000 0.04000)      1   \n",
       "1         2  rect_jj  LINESTRING (0.04000 -0.90000, -0.04000 -0.90000)      1   \n",
       "2         3  rect_jj  LINESTRING (-2.55000 -0.04000, -2.55000 0.04000)      1   \n",
       "3         4  rect_jj    LINESTRING (0.04000 0.90000, -0.04000 0.90000)      1   \n",
       "\n",
       "   subtract  helper  chip  width hfss_inductance  hfss_capacitance  \\\n",
       "0     False   False  main   0.02            10nH                 0   \n",
       "1     False   False  main   0.02            10nH                 0   \n",
       "2     False   False  main   0.02            10nH                 0   \n",
       "3     False   False  main   0.02            10nH                 0   \n",
       "\n",
       "   hfss_resistance  hfss_mesh_kw_jj q3d_inductance  q3d_capacitance  \\\n",
       "0                0         0.000007           10nH                0   \n",
       "1                0         0.000007           10nH                0   \n",
       "2                0         0.000007           10nH                0   \n",
       "3                0         0.000007           10nH                0   \n",
       "\n",
       "   q3d_resistance  q3d_mesh_kw_jj      gds_cell_name  \n",
       "0               0        0.000007    FakeJunction_02  \n",
       "1               0        0.000007    FakeJunction_02  \n",
       "2               0        0.000007    FakeJunction_01  \n",
       "3               0        0.000007  my_other_junction  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "design.qgeometry.tables['junction']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let us look at all the polygons used to create qubit `q1`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Poly table hold the polygons identified from QComponents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>component</th>\n",
       "      <th>name</th>\n",
       "      <th>geometry</th>\n",
       "      <th>layer</th>\n",
       "      <th>subtract</th>\n",
       "      <th>helper</th>\n",
       "      <th>chip</th>\n",
       "      <th>fillet</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>pad_top</td>\n",
       "      <td>POLYGON ((2.33750 0.04000, 2.76250 0.04000, 2....</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>main</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>pad_bot</td>\n",
       "      <td>POLYGON ((2.33750 -0.13000, 2.76250 -0.13000, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>main</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>rect_pk</td>\n",
       "      <td>POLYGON ((2.22500 -0.32500, 2.87500 -0.32500, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>main</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>a_connector_pad</td>\n",
       "      <td>POLYGON ((2.63750 0.14500, 2.76250 0.14500, 2....</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>main</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>b_connector_pad</td>\n",
       "      <td>POLYGON ((2.46250 0.14500, 2.33750 0.14500, 2....</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>main</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>c_connector_pad</td>\n",
       "      <td>POLYGON ((2.56250 -0.14500, 2.76250 -0.14500, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>main</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>d_connector_pad</td>\n",
       "      <td>POLYGON ((2.46250 -0.14500, 2.33750 -0.14500, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>main</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  component             name  \\\n",
       "0         1          pad_top   \n",
       "1         1          pad_bot   \n",
       "2         1          rect_pk   \n",
       "3         1  a_connector_pad   \n",
       "4         1  b_connector_pad   \n",
       "5         1  c_connector_pad   \n",
       "6         1  d_connector_pad   \n",
       "\n",
       "                                            geometry  layer  subtract  helper  \\\n",
       "0  POLYGON ((2.33750 0.04000, 2.76250 0.04000, 2....      1     False   False   \n",
       "1  POLYGON ((2.33750 -0.13000, 2.76250 -0.13000, ...      1     False   False   \n",
       "2  POLYGON ((2.22500 -0.32500, 2.87500 -0.32500, ...      1      True   False   \n",
       "3  POLYGON ((2.63750 0.14500, 2.76250 0.14500, 2....      1     False   False   \n",
       "4  POLYGON ((2.46250 0.14500, 2.33750 0.14500, 2....      1     False   False   \n",
       "5  POLYGON ((2.56250 -0.14500, 2.76250 -0.14500, ...      1     False   False   \n",
       "6  POLYGON ((2.46250 -0.14500, 2.33750 -0.14500, ...      1     False   False   \n",
       "\n",
       "   chip fillet  \n",
       "0  main    NaN  \n",
       "1  main    NaN  \n",
       "2  main    NaN  \n",
       "3  main    NaN  \n",
       "4  main    NaN  \n",
       "5  main    NaN  \n",
       "6  main    NaN  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q1.qgeometry_table('poly')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Paths are lines. These can have a width."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th>chip</th>\n",
       "      <th>width</th>\n",
       "      <th>fillet</th>\n",
       "      <th>hfss_wire_bonds</th>\n",
       "      <th>q3d_wire_bonds</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>a_wire</td>\n",
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       "      <td>1</td>\n",
       "      <td>b_wire_sub</td>\n",
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       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>c_wire</td>\n",
       "      <td>LINESTRING (2.76250 -0.15500, 2.78750 -0.15500...</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
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       "      <td>0.010</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>c_wire_sub</td>\n",
       "      <td>LINESTRING (2.76250 -0.15500, 2.78750 -0.15500...</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>main</td>\n",
       "      <td>0.022</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>d_wire</td>\n",
       "      <td>LINESTRING (2.33750 -0.15500, 2.31250 -0.15500...</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>main</td>\n",
       "      <td>0.010</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>d_wire_sub</td>\n",
       "      <td>LINESTRING (2.33750 -0.15500, 2.31250 -0.15500...</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>main</td>\n",
       "      <td>0.022</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  component        name                                           geometry  \\\n",
       "0         1      a_wire  LINESTRING (2.76250 0.15500, 2.78750 0.15500, ...   \n",
       "1         1  a_wire_sub  LINESTRING (2.76250 0.15500, 2.78750 0.15500, ...   \n",
       "2         1      b_wire  LINESTRING (2.33750 0.15500, 2.31250 0.15500, ...   \n",
       "3         1  b_wire_sub  LINESTRING (2.33750 0.15500, 2.31250 0.15500, ...   \n",
       "4         1      c_wire  LINESTRING (2.76250 -0.15500, 2.78750 -0.15500...   \n",
       "5         1  c_wire_sub  LINESTRING (2.76250 -0.15500, 2.78750 -0.15500...   \n",
       "6         1      d_wire  LINESTRING (2.33750 -0.15500, 2.31250 -0.15500...   \n",
       "7         1  d_wire_sub  LINESTRING (2.33750 -0.15500, 2.31250 -0.15500...   \n",
       "\n",
       "   layer  subtract  helper  chip  width fillet  hfss_wire_bonds  \\\n",
       "0      1     False   False  main  0.010    NaN            False   \n",
       "1      1      True   False  main  0.022    NaN            False   \n",
       "2      1     False   False  main  0.010    NaN            False   \n",
       "3      1      True   False  main  0.022    NaN            False   \n",
       "4      1     False   False  main  0.010    NaN            False   \n",
       "5      1      True   False  main  0.022    NaN            False   \n",
       "6      1     False   False  main  0.010    NaN            False   \n",
       "7      1      True   False  main  0.022    NaN            False   \n",
       "\n",
       "   q3d_wire_bonds  \n",
       "0           False  \n",
       "1           False  \n",
       "2           False  \n",
       "3           False  \n",
       "4           False  \n",
       "5           False  \n",
       "6           False  \n",
       "7           False  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q1.qgeometry_table('path')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The junction table is handled differently by each QRenderer.\n",
    "\n",
    "###  What does GDS do with \"junction\" table?\n",
    "This is better explained in folder 5 All QRenderers/5.2 GDS/GDS QRenderer  notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
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       "      <th>hfss_capacitance</th>\n",
       "      <th>hfss_resistance</th>\n",
       "      <th>hfss_mesh_kw_jj</th>\n",
       "      <th>q3d_inductance</th>\n",
       "      <th>q3d_capacitance</th>\n",
       "      <th>q3d_resistance</th>\n",
       "      <th>q3d_mesh_kw_jj</th>\n",
       "      <th>gds_cell_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>rect_jj</td>\n",
       "      <td>LINESTRING (2.55000 -0.04000, 2.55000 0.04000)</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>main</td>\n",
       "      <td>0.02</td>\n",
       "      <td>10nH</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>10nH</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>FakeJunction_02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  component     name                                        geometry  layer  \\\n",
       "0         1  rect_jj  LINESTRING (2.55000 -0.04000, 2.55000 0.04000)      1   \n",
       "\n",
       "   subtract  helper  chip  width hfss_inductance  hfss_capacitance  \\\n",
       "0     False   False  main   0.02            10nH                 0   \n",
       "\n",
       "   hfss_resistance  hfss_mesh_kw_jj q3d_inductance  q3d_capacitance  \\\n",
       "0                0         0.000007           10nH                0   \n",
       "\n",
       "   q3d_resistance  q3d_mesh_kw_jj    gds_cell_name  \n",
       "0               0        0.000007  FakeJunction_02  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q1.qgeometry_table('junction')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Geometric boundary of a QComponent?\n",
    "Return the boundry box of the geometry, for example: `q1.qgeometry_bounds()`.  \n",
    "The function returns a tuple containing (minx, miny, maxx, maxy) bound values\n",
    "for the bounds of the component as a whole."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Q1         : [ 2.125 -0.325  2.975  0.325]\n",
      "Q2         : [-0.325 -1.325  0.325 -0.475]\n",
      "Q3         : [-2.975 -0.325 -2.125  0.325]\n",
      "Q4         : [-0.325  0.475  0.325  1.325]\n",
      "cpw1       : [ 0.22       -0.54399198  2.125      -0.07600802]\n",
      "cpw2       : [-2.125      -0.55810289 -0.22       -0.06189711]\n",
      "cpw3       : [-2.125       0.07552405 -0.22        0.54447595]\n",
      "cpw4       : [0.22       0.07576603 2.125      0.54423397]\n"
     ]
    }
   ],
   "source": [
    "for name, qcomponent in design.components.items():\n",
    "    print(f\"{name:10s} : {qcomponent.qgeometry_bounds()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Qiskit Metal Version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Qiskit Metal        0.0.3\n",
      "\n",
      "Basic\n",
      "____________________________________\n",
      " Python              3.7.8 | packaged by conda-forge | (default, Nov 27 2020, 18:48:03) [MSC v.1916 64 bit (AMD64)]\n",
      " Platform            Windows AMD64\n",
      " Installation path   c:\\workspace\\qiskit-metal\\qiskit_metal\n",
      "\n",
      "Packages\n",
      "____________________________________\n",
      " Numpy               1.19.5\n",
      " Qutip               4.5.3\n",
      "\n",
      "Rendering\n",
      "____________________________________\n",
      " Matplotlib          3.3.4\n",
      "\n",
      "GUI\n",
      "____________________________________\n",
      " PySide2 version     5.13.2\n",
      " Qt version          5.9.7\n",
      " SIP version         4.19.8\n",
      "\n",
      "IBM Quantum Team\n"
     ]
    }
   ],
   "source": [
    "metal.about();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# gui.main_window.close()"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Tags",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
